Abstract
The nonlocal-means(NLM) is a denoising algorithm which takes advantage of the redundancy of similar patches in the image. While producing state-of-the-art denoising results, the NLM algorithm has high computational complexity. In [1] Ce Liu et al. introduced approximate K-nearest neighbors(AKNN) matching to classical NLM for reducing the complexity of the algorithm. In this paper an improved AKNN-NLM algorithm with NVIDIA GPU acceleration is proposed. The experiments show that the improved GPU based AKNN-NLM algorithm have excellent performance on both image and video denoising. The GPU based implementation is up to 40 times faster than the CPU counterparts.
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Jin, W., Qi, J. (2013). An Improved Approximate K-Nearest Neighbors Nonlocal-Means Denoising Method with GPU Acceleration. In: Yang, J., Fang, F., Sun, C. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2012. Lecture Notes in Computer Science, vol 7751. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36669-7_52
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DOI: https://doi.org/10.1007/978-3-642-36669-7_52
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